{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 皮马印第安人糖尿病数据集分类器练习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic 回归&SVM\n",
    "\n",
    "直接把任务描述和数据说明放在开头，省的再去翻说明"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、 任务描述 \n",
    "皮马印第安人糖尿病数据集 进行分类器练习。 需要提交代码文件，并给出必要的结果解释。\n",
    "\n",
    "1) 训练数据和测试数据分割(随机选择 20%的数据作为测试集);(10 分)\n",
    "\n",
    "2) 适当的特征工程(及数据探索);(10 分)\n",
    "\n",
    "3) Logistic 回归，并选择最佳的正则函数(L1/L2)及正则参数;(30 分)\n",
    "\n",
    "4) 线性 SVM，并选择最佳正则参数，比较与 Logistic 回归的性能，简单说明原因。(20 分)\n",
    "\n",
    "5) RBF 核的 SVM，并选择最佳的超参数(正则参数、RBF 核函数宽度);(30 分)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、 数据说明\n",
    "原始数据集地址:https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes\n",
    "数据集只有一个文件(diabetes.csv):Pima Indians Diabetes Dataset 包括根据医疗 记录的比马印第安人 5 年内糖尿病的发病情况，这是一个两类分类问题。每个类的 样本数目数量不均等。一共有 768 个样本，每个样本有 8 个输入变量和 1 个输出 变量。缺失值通常用零值编码。\n",
    "\n",
    "1) 字段说明\n",
    "\n",
    "Pregnancies: 怀孕次数\n",
    "\n",
    "Glucose: 口服葡萄糖耐受试验中，2 小时的血浆葡萄糖浓度。 \n",
    "\n",
    "BloodPressure: 舒张压(mm Hg)\n",
    "\n",
    "SkinThickness: 三头肌皮肤褶层厚度(mm)\n",
    "\n",
    "Insulin:2 小时血清胰岛素含量(μU/ ml)\n",
    "\n",
    "BMI: 体重指数(体重，kg /(身高，m)^ 2)\n",
    "\n",
    "2) DiabetesPedigreeFunction: 糖尿病家族史\n",
    "\n",
    "3) Age: 年龄(岁)\n",
    "\n",
    "输出 Outcome: 输出变了/类别标签(0 或 1，出现糖尿病为 1, 否则为 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 首先 import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#竞赛的评价指标为logloss\n",
    "from sklearn.metrics import log_loss  \n",
    "#SVM并不能直接输出各类的概率，所以在这个例子中我们用正确率作为模型预测性能的度量\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3、数据读取&数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "dpath = './data/diabetes.csv'\n",
    "train = pd.read_csv(dpath)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可见其中的缺失值确实都使用了0进行填补\n",
    "\n",
    "其中：2小时葡萄糖浓度、舒张压、三头肌皮肤褶层厚度、2小时血清胰岛素含量、体重指数不可能为0，判断这些特征取0的都为缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose            5\n",
      "BloodPressure     35\n",
      "SkinThickness    227\n",
      "Insulin          374\n",
      "BMI               11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "统计以上几个特征出现0的情况，有统计可知：三头肌皮肤褶层厚度、2小时血清一倒数含量出现0值得情况较多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看特征分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c903d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 这是一个两类分类问题，所以输出只有两个，0代表没得糖尿病的，1代表得糖尿病的\n",
    "# 查看输出的分布分布，可以看出得糖尿病的真不少...\n",
    "sns.countplot(train.Outcome);\n",
    "pyplot.xlabel('Outcome');\n",
    "pyplot.ylabel('Number of Outcome');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9m5kNbmV6Mf0KOJ909/TS5oZjZmbtokyCeDMiftj0SMzMrK2USRD/Kulk4Fbgre7CiJjd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\nQZiZWSEnCDMzK+QEYWZmhZwgzMys0P8HPt1D7EV57wEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20171cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "#怀孕次数，这个十几次怀孕的都是认真的吗？\n",
    "sns.countplot(train.Pregnancies)\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a203e6240>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EZv51ZnZm5ijgWGB2Zk4Bfg4cXW52InBjX+cmSXpFO50H8XngzIh4kGJM4pIW5yNJA1pL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\nklTJAiFJqmSBkCRVskBIkir9f2ahumjQcYd0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2027a518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#这里是借鉴老师的代码，查看怀孕与是否得病的关系\n",
    "sns.countplot(x=\"Pregnancies\", hue=\"Outcome\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其实咨询了学医的朋友，糖尿病确实与怀孕有关，怀孕次数越多，得糖尿病的几率越大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eN3+TmmDnJpHcie8pYDPwNeA14N9dnFcMZHd4nwUc6K6M86STAlT2cG4xUKyq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\nAYC2TS97J6aiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20642cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#两小时葡萄糖浓度\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Glucose, kde = True)\n",
    "plt.xlabel('Glucose')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的0应该是缺失值，这个分布大致是个正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of BloodPressure')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i8Y+tsJl3JpONh06yvDgHjyc+Z0Ieydz8NF7eX0dXb791dogDwbS51A4kFlcF\nUBumeIyZtKoaO6hq7GRlSU6kQ4lKJfnp+BUOn2yPdChmAgx75yIi/+BulonIeuBhnDaXa3FG6Rtj\nAmyscFboXlGSG+FIotOsnFQSPEJFXTuLpmVGOhwTZiNVi70/YPsEcIm7XQdYVxhjBtlQcZLsVB8L\n3XVMzNv5vB5m5Vq7S7wYNrmo6icnMhBjJruNhxqsvWUU8/LTeXb3Cdq7+0hLir/ln+NJML3F5gBf\nAIoDy6vqNeELy5jJpbqpk6MNHdxwfnGkQ4lqJfnpwAkq6ts5w6bHiWnBfHX4E3AXzqh8G15rzBA2\nHjoJwIo51pg/kpnZKSQleKioa7PkEuOCSS5dqvqzsEdizCS2saKBzOQETptuDdUj8XqE4tw0DtZZ\nj7FYF0xX5J+KyLdEZJWInDPwCObiIrJaRPaJSLmI3DrE8SQRecg9vlFEit39uSLyooi0icjPB51z\nrojsdM/5mYhYBbeJKFXl7wdPsnxODl5rbxnV3Pw06tu6ae7sjXQoJoyCuXM5A/hH4HLeqhZT9/mw\nRMQL/AK4EqgCNovIOlXdHVDsRqBRVeeJyFrgDuCjQBfwTWCJ+wj0K+AmYAPOPGersQGdJoIO1rVz\ntKGDT19cEulQJoUSd/qXCus1FtOCSS4fBEoCp90P0nKgXFUrAETkQWANEJhc1gD/4W4/CvxcRERV\n24G/ici8wAuKyHQgU1Vfd5//HvgAllxMBL241xlTfPmi2FvmaLQZJMZjWlYyqYleqxqLccFUi20H\nxjML30ygMuB5lbtvyDKq2gc0AyONQJvpXmekaxozoV7YW8vCqRnMzE6JdCiTgkeEkrw0KurasDlw\nY1cwyWUqsFdEnhGRdQOPIM4bqvJ58G9SMGXGVV5EbhKRUhEpraurG+GSxoxfS1cvmw83cFkM3rWE\nU0l+Ok2dvTYFfwwLplrsW+O8dhVQFPC8EKgepkyVu8JlFtAwyjULR7kmAKp6J3AnwLJly+zrkQmL\nvx2op8+vMVklFk4D0+6/Vn6S2bk2g3QsGvXORVVfHuoRxLU3A/NFZI6IJAJrgcF3POuAG9ztDwMv\njLRWjKrWAK0istLtJfYJ4IkgYjEmLF7YW0tWio9zZtn6LWORl55IZnICfz9YH+lQTJgEM0K/lbeq\nnhIBH9CuqiN26FfVPhG5BXgG8AK/U9UyEbkdKFXVdTiDM+8TkXKcO5a1Aa97GMgEEkXkA8C73Z5m\nnwPuAVJwGvKtMd9EhN+vvLSvlosX5JPgDaaG2QwQEUry03n94ElUFRtREHuCWYnybbPwuX/olwdz\ncVVdj9NdOHDfbQHbXTizLA8484sWAAAaeUlEQVR1bvEw+0t5Z/dkYybc9qom6tt6uHxRfqRDmZTm\n5qexrbKJ/SfaWDjNJvuMNWP+uqWqf2KUMS7GxIMnt9eQ6PVw+aKpkQ5lUhoY72JVY7EpmGqxfwh4\n6gGWMXKPLmNiXr9f+fOOai5ZmE9Wii/S4UxKU1ITmZ2bymvlJ/nkBXMiHY4JsWB6iwWu69IHHMYZ\n/GhM3Np46CS1rd1cc9aMSIcyqZ0/N5c/b6+hr99v7VYxJpg2F1vXxZhBntxeTWqil3edZlVip2LV\n3Dwe2FRJWXULZxVZj7tYMtIyx7cNdwxQVf1OGOIxJur19PlZv/M4Vy6eyuNbj0U6nEltlbsk9N8P\nnrTkEmNGug9tH+IBzmST/xbmuIyJWq8eqKO5s9eqxEIgPyOJBVPTrVE/Bo20zPGPBrZFJAP4EvBJ\n4EHgR8OdZ0wsGWrixgc2HSXF5+VYUycJHmsnOFXnz83jwc1H6e7rJynBG+lwTIiM+MkQkRwR+S6w\nAycRnaOq/6aqtRMSnTFRpq27j901LSydlW2JJUTOn5tLV6+fbUebIh2KCaFhPx0i8n9xpnBpBc5Q\n1f9Q1cYJi8yYKLT1aCP9fuW8YlvOOFRWlOTiEafdxcSOkb56/TMwA/gGUC0iLe6jVURaJiY8Y6KH\nqrL5cAOzclKZlpkc6XBiRlaKjyUzs3jdkktMGTa5qKpHVVNUNUNVMwMeGaPNK2ZMLDpU3059Ww/L\n7a4l5FbNzWVrZSMdPX2RDsWEiFUaGxOkTYcbSPZ5OKMwK9KhxJzz5+bR269sPmw177HCkosxQWjr\n7qOsuoWlRVPw2UjykDuveAo+r1iX5BhinxJjgrDp0En6/crKEqsSC4fUxATOLppi7S4xxJKLMaPo\n8/vZWNHAgqnpFGRYQ364rJqby85jzTR39EY6FBMCllyMGcXOqmZau/s4f25epEOJaRcvyEMVXtpv\nw+higSUXY0agqvz94Eny05OYX5Ae6XBi2tKiKeSlJ/Hs7hORDsWEgCUXY0ZwtKGDY02dnD8v15bi\nDTOvR7hycQEv7a2lu68/0uGYU2TJxZgRvLK/jhSfl7OLpkQ6lLjw7sXTaO/pt9H6McCSizHDOHCi\nlT3HW1k1N5fEBPuoTIRVc3NJS/TynFWNTXr2iTFmGL9+pQKfV95cc8SEX7LPy6ULC3hu9wn8fltN\nfTILa3IRkdUisk9EykXk1iGOJ4nIQ+7xjSJSHHDsa+7+fSLynoD9h0Vkp4hsE5HScMZv4ldNcydP\nbDvGstk5pCUFsxq4CZV3nz6VutZutlXZLMmTWdiSi4h4gV8AVwGLgetEZPGgYjcCjao6D/gJcId7\n7mJgLXA6sBr4pXu9AZep6lJVXRau+E18u+vVQ/gVLpxn3Y8n2qULC0jwCE/vOh7pUMwpCOedy3Kg\nXFUrVLUHZ5GxNYPKrAHudbcfBa4Qp0vOGuBBVe1W1UNAuXs9Y8KuqaOH+zcd5f1nTmdKWmKkw4k7\nWSk+Ll1YwJ+2HqOv3x/pcMw4hfN+fyZQGfC8ClgxXBlV7RORZiDX3b9h0Lkz3W0FnhURBX6tqneG\nIXYTx+57/QgdPf185pK5bLUFrMJqqJU+AaZmJlHb2s2r5fVctrBggqMyoRDOO5ehBgUMbqEbrsxI\n516gqufgVLfdLCIXD/niIjeJSKmIlNbV1QUb84RobO/ht69WcNVPX+WBTUdp7bLpLqJFV28/9/z9\nMJcuzOe06bayRKQsnJZBaqKXR0urIh2KGadwJpcqoCjgeSFQPVwZEUkAsoCGkc5V1YF/a4HHGaa6\nTFXvVNVlqrosPz//lH+YUNlQcZJV//k8331qDwkeYU9NCz99/gC7jjVHOjQDPFJaycn2Hj53ydxI\nhxLXEjwezirK5rndJ2jq6Il0OGYcwplcNgPzRWSOiCTiNNCvG1RmHXCDu/1h4AVVVXf/Wrc32Rxg\nPrBJRNJEJANARNKAdwO7wvgzhFRjew9ffnAbM7JSePrLF/HkFy7k5svmkZOWyP2bjnLkZHukQ4xr\nff1+7ny1grNnZbN8js1+HGnnzppCT7+fJ7cP/k5qJoOwJRdV7QNuAZ4B9gAPq2qZiNwuIte4xe4C\nckWkHPgKcKt7bhnwMLAbeBq4WVX7ganA30RkO7AJeEpVnw7XzxBKqspX/3cHDe09/Oy6s1k0zaly\nmZqZzI0XzCErxcfjW4/R57cGzEhZt72ayoZOPnvJXJvqJQrMyE7htOmZPGxVY5NSWDvwq+p6YP2g\nfbcFbHcB1w5z7veA7w3aVwGcFfpIw+/+TUd5bvcJvnn1YpbMfPtKhkk+L9ecNYP7Nhzhlf31XL7I\nGjAnWl+/n589f4DTpmdy5WlTIx2OcV23vIjbnihj06EGu5ucZGyE/gTo6OnjR8/uZ1VJLp+6oHjI\nMqdNz2TJzCxe3FdLfWv3xAZoeGJbNYdPdvDld83H47G7lmhx7blF5KYl8suXyiMdihkjSy4T4L7X\nj9DQ3sO/vGfhiNUtV585Ha9H+Otem1dpIvX1+/nvFw5w+oxM3r3Y7lqiSUqil09dOIeX9tVRVm2d\nXiYTSy5h1tHTx52vVHDR/DzOnT3yzLqZyT5WzslhZ1Uz9W129zJRHt96zL1rWWBtLVHo4ytnk56U\nwC9fOhjpUMwYWHIJsz9uOMrJ9h6+dMX8oMpfMC8Pr0d4eX90jc2JVV29/fzkuf2cWZjFu06ztq5o\nlJXi4+MrZ7N+Zw0VdW2RDscEyZJLGHX19vPrVw5y4bw8lhUH1xiZkezjvOIcth5tpNH694fdXX87\nRHVzF//+3tPsriWK3XjhHFJ9Xr795G6c0Qom2llyCaNHt1RR39bDLZfPG9N5F83PQxBesbuXsKpv\n6+ZXLx3kysVTWWnT6ke1/Iwk/vndC3l5fx3rd9qElpOBJZcw6fcrd/3tEGcVZrFijF0os1MTOWd2\nNluONFLb0hWmCM1//XU/nb393HrVokiHYoLwiVWzWTIzk28/WWZTJk0CllzC5K97TnCovp1PX1wy\nruqWSxYU4FflN69WhCE6s+tYMw9squT6FbOYm58e6XBMEBK8Hr73gTOoa+vm+3/ZG+lwzCgsuYTJ\nb16poHBKCqtPnzau83PSEjmzMJs/bDhKQ7u1vYRSb7+frz66g5y0RP753QsjHY4Zg7OKsrnpohLu\n33h02BmVTXSw5BIGW440UnqkkRsvnEOCd/xv8SUL8unq6+fu1w6FMDrz21cPsbumhe+sOZ2sFF+k\nwzFj9K/vWcilC/O57Yld/P1gfaTDMcOw9VvD4BcvlpOd6uMjy4pGLzyCqZnJXLVkGve8dpj/c1GJ\n/SEMgYq6Nv7rr/tZffo0Vi+Zbt9+o9xw/z8Xz8+nrLqFz/3hDe755HmcPWvkMWRm4tmdS4htr2zi\nhb21fPqikpCsvf75S+fR2t3Hfa8fPuVrxbvOnn4+/8c3SEn0cvua0yMdjjkFyT4vN6wqJjMlgbV3\nbuDpXTWRDskMYsklxH72/AGyUnx8YtXskFxvycwsLl9UwF1/O0R7d19IrhmPVJWvP76TfSda+ena\nsynITI50SOYU5aQl8vjnL2DxjEw+98c3+P5f9thnJIpYcgmhHVVNPL+3lk9fNIeM5NBVYd182Twa\nO3p5YJNV4YzXHzYc4bGtx/jyFQu4ZEH0LB5nTk1eehIPfHolHzm3iF+/XMHlP3qJh0sr6ertj3Ro\ncc+SSwj95Ln9ZKX4uOH84pBe99zZU7hgXi6/fqXCPjTj8MS2Y3xrXRmXLyrgC2Mc0GqiX7LPyx0f\nPpP//dz5FGQk89VHd3Ded//K1x7bwVM7aqhp7ox0iHHJGvRD5Jmy47y4r45br1oU0ruWATdfNo+P\n/WYjD22uDHnyimXrd9bwlYe3c15xDr/42Dk2nX4MO3f2FJ64+QI2HDrJo6VV/GlrNQ9sqgQgxedl\nSpqPKamJTElNJDv1re2ctEQSE4b/nv2xFbMm6keIKZZcQqC1q5dvPVHGomkZ3HjhnLC8xqqSXFaV\n5PLDZ/bxrsVTmZmdEpbXiRWqyv2bjvKtJ8o4uyib3/3TeaQkeiMdlgkzj0c4f24e58/N4z8/5Ocn\nz+3naEMH9W3dNHb0UNvazf4TrfT2vzU/mQAFmUkUTkllXkE6Cwoy7HclBCy5hMCPnt3PidYufvXx\nc/CdwriWkYgId3zoTFb/9BW++uh27vvUCvsWPoyu3n6+9UQZD5VWsmBqOu89YzpPbLN12GPRaF3J\ni3JSKcpJfds+VaW9p5/G9p43E05VYwe7q1vYcqQRAebmp3Pu7CksnpEZxuhjmyWXU/TK/jruff0w\nn1g5O+x97WflpvKN9y3m3x/fyX0bjlj12BD+frCe254oo7y2jVsum8e0rGQ8NtuxCSAipCclkJ6U\n8LbE41elqrGTvcdb2F7ZxEOllST7PBysa+Mjy4resTy5GZkll1Owo6qJz/5hC4umZfKvqydm8sPr\nlhfx7O7jfG/9Hkry07hovvV8AthT08LPXyznqR01FOWkcPcnz+OyhQU2SNIEzSPCrJxUZuWk8q7T\nplJR107pkQYe3FzJ718/wpKZmaw9bxZrls4IS7tqrJF4WBth2bJlWlpaGtJrVtS1ce3/vE5KopfH\nPnf+uMdNjPbHb6jGxIb2Hj72mw1U1Lfzm08si9uute3dfbywt5aHSyt59UA9qYle/s+Fc/j8ZfNI\n9jl15pZczKl67xnTeGJbNQ9sOsre462k+LxcfeZ01i6fxTmzsmN6HSAR2aKqy8ZzbljvXERkNfBT\nwAv8VlX/c9DxJOD3wLnASeCjqnrYPfY14EagH/iiqj4TzDXDTVX53zeO8e11ZfgSPPz+U8snfEBe\nTloiD3x6Jdf/diOfvreUb75/MR9bPgtvFLbBjCd5DkVVqWvtpryuja1Hm9hypJHXyuvp7vMzNTOJ\nf33PQj6+YjZZqfaN0oRWdmoiN5xfzCdWzWZ7VTMPbjrKuu3VPLKlipnZKaxeMo3LFhZwzuxsUhOt\nMmhA2O5cRMQL7AeuBKqAzcB1qro7oMzngTNV9bMishb4oKp+VEQWAw8Ay4EZwF+BBe5pI15zKKG4\nc/H7ldcrTnLX3w7xwt5alhfn8MNrz2JWburoJ4/gVP74NnX0cPP9b/Ba+UlOn5HJV1cv4oK5uac0\nWeZY9Pb7OdnWQ11rN/Xt3TS299DgPho7ejjZ1sO+E6109vTT51f6/Upfv59+VfwKXhHSkhLweQWf\n10OCV/B5PPgShASPB59X6Oztp6Wzj/q2bjp63hrjM68gnQvm5vLeM6azrDhn2MRqdy7mVA31GWzr\n7mP9zhqe2XWcVw/U09PvJ8EjLJmZxaJpGcwrSKdwSipTM5PISUskJdFLis95TNTnMxRO5c4lnMll\nFfAfqvoe9/nXAFT1+wFlnnHLvC4iCcBxIB+4NbDsQDn3tBGvOZTxJpctRxrZdayZsupmXis/ybGm\nTjKSE/jC5fO48cKSkNwpnOo3e1Xlzztq+N5Tezje0sWUVB+XLSpg8fRM5hakk5+eRGqil7SkBFIS\nvaT6vCjOYmZ9fqW/X+nz++n3K71+pbOnj9auPtq6+2jr6qOlq5f6th7q27qdf1u7qWvrpr6tm6aO\noRds8gikJSaQlpRAaqKXlEQvPq8Hr0fweoQEjyBAv0JJfhq9fX56+/30+pXePj99fnWe9/tpbO8l\n2echLSmB3PQkctMSKcxOITUE87YZEwrXLJ1B6eEGNh5q4I0jjRyobRtxmQyvR978QuXzeijISCLZ\n5yXZ5yHF5yU1MYGM5IGHb9C/CWS620kJXucLmddDovv58us7P9f5GUnjrrqL1mqxmUBlwPMqYMVw\nZVS1T0SagVx3/4ZB5850t0e7Zsh89dHtHKxrJyctkbMKs/jq6oW85/Rpb9bnRwMR4f1nzeDKxVN5\naV8dT++q4cW9tTz2xrGQvk56UgJ56YnkZyQxvyCdVSW55KUncai+3e154ySwtKQEkhI8Qf8yj5Y8\n7c7DRLv0pAQuXVjApQsL3tx3sq2bmuYualu7WL/jOL1+Pz3ul6ievre+PPX2+5mamUxnbz/dvX7q\n23po7+mgtauP1q5eunr9pxzf3u+sjsjfrHAml6H+ugy+TRquzHD7h7qfHPLWS0RuAm5yn7aJyL5h\n4hzVEWArcM94LzCyPGDYRSmuD89rjseIcY5XGH6+sMQZBhZnaEUszjH+Dk94nCl3jPvUPGDcM/CG\nM7lUAYELmhQCg0eyDZSpcqvFsoCGUc4d7ZoAqOqdwJ3jDX6iiEjpeG87J5LFGVoWZ2hZnKHnxlo8\n3vPD2bK0GZgvInNEJBFYC6wbVGYdcIO7/WHgBXUagdYBa0UkSUTmAPOBTUFe0xhjTISF7c7FbUO5\nBXgGp9vw71S1TERuB0pVdR1wF3CfiJTj3LGsdc8tE5GHgd1AH3CzqvYDDHXNcP0MxhhjxiesXW5U\ndT2wftC+2wK2u4Brhzn3e8D3grnmJBf1VXcuizO0LM7QsjhD75RijYsR+sYYYybW5BnNY4wxZtKw\n5BIhIrJaRPaJSLmI3BrpeAaISJGIvCgie0SkTES+5O7PEZHnROSA+294p4AOkoh4RWSriPzZfT5H\nRDa6cT7kdvyIdIzZIvKoiOx139dVUfx+/n/u//suEXlARJKj4T0Vkd+JSK2I7ArYN+R7KI6fuZ+t\nHSJyToTj/L/u//0OEXlcRLIDjn3NjXOfiLwnknEGHPsXEVERyXOfj+v9tOQSAe7UOL8ArgIWA9e5\nU95Egz7gn1X1NGAlcLMb263A86o6H3jefR4NvgTsCXh+B/ATN85GnPnpIu2nwNOqugg4CyfeqHs/\nRWQm8EVgmaouwek0s5boeE/vAVYP2jfce3gVTg/T+Thj3X41QTHC0HE+ByxR1TNxpq/6GoD7uVoL\nnO6e80v3b0Ok4kREinCm1wocvTyu99OSS2QsB8pVtUJVe4AHgTURjgkAVa1R1Tfc7VacP4QzceK7\n1y12L/CByET4FhEpBN4H/NZ9LsDlwKNukYjHKSKZwMU4PSNR1R5VbSIK309XApDijjtLBWqIgvdU\nVV/B6VEaaLj3cA3we3VsALJFZHqk4lTVZ1W1z326AWd83kCcD6pqt6oeAspx/jZEJE7XT4Cv8vbB\n6eN6Py25RMZQU+PMHKZsxIhIMXA2sBGYqqo14CQgoGD4MyfMf+F8EAbmyMgFmgI+yNHwvpYAdcDd\nbvXdb0UkjSh8P1X1GPBDnG+tNUAzsIXoe08HDPceRvPn61PAX9ztqIpTRK4Bjqnq9kGHxhWnJZfI\nCGZqnIgSkXTgf4Evq2pLpOMZTESuBmpVdUvg7iGKRvp9TQDOAX6lqmcD7URBFdhQ3DaLNcAcnNnI\n03CqRAaL9Hs6mmj8PUBEvo5T7fzHgV1DFItInCKSCnwduG2ow0PsGzVOSy6REczUOBEjIj6cxPJH\nVX3M3X1i4FbY/bc2UvG5LgCuEZHDONWKl+PcyWS7VToQHe9rFVClqhvd54/iJJtoez8B3gUcUtU6\nVe0FHgPOJ/re0wHDvYdR9/kSkRuAq4Hr9a3xH9EU51ycLxXb3c9UIfCGiExjnHFacomMqJ3Gxm23\nuAvYo6o/DjgUOFXPDcATEx1bIFX9mqoWunMfrcWZOuh64EWcqYQgOuI8DlSKyEJ31xU4M09E1fvp\nOgqsFJFU9/dgINaoek8DDPcergM+4fZyWgk0D1SfRYI4Cxz+G3CNqnYEHBpumqsJp6o7VbVAVYvd\nz1QVcI77+zu+91NV7RGBB/BenJ4jB4GvRzqegLguxLnl3QFscx/vxWnPeB444P6bE+lYA2K+FPiz\nu12C8wEtBx4BkqIgvqVAqfue/gmYEq3vJ/BtYC+wC7gPSIqG9xRn8cAaoNf9w3fjcO8hTjXOL9zP\n1k6c3m+RjLMcp81i4PP0PwHlv+7GuQ+4KpJxDjp+GMg7lffTRugbY4wJOasWM8YYE3KWXIwxxoSc\nJRdjjDEhZ8nFGGNMyFlyMcYYE3KWXEzcEZF+EdkmIttF5A0ROd/dXzzULLHjfI2XRGSZu31YRHa6\nr/esOzDNmJhmycXEo05VXaqqZ+HMUPv9CXjNy9zXKwX+ffDBCZwNd0Jfy8QvSy4m3mXiTCP/Nu46\nJne7dxxbReSyUfaniMiD7noXDwEpw7zeK8A895w2EbldRDYCq0TkXBF5WUS2iMgzAVObfFFEdrvX\nftDdd4l797XNjSNDRC4Vd10bt8zPReSf3O3DInKbiPwNuFZE5orI0+5rvSoii0L0fhoDOJPqGRNv\nUkRkG5AMTMeZl2ywmwFU9Qz3D++zIrJghP2fAzpU9UwRORN4Y5jXvhpnlDM4E0PuUtXb3PncXgbW\nqGqdiHwU+B7OLLq3AnNUtVveWmjqX4CbVfU1d5LRriB+7i5VvRBARJ4HPquqB0RkBfDLYd4HY8bF\nkouJR52quhRARFYBvxeRJYPKXAj8N4Cq7hWRI8CCEfZfDPzM3b9DRHYMut6LItKPMwXMN9x9/TgT\nhAIsBJYAzznTeuHFmZ4D95w/isifcKaPAXgN+LGI/BF4TFWr3PNG8pD7M6fjTEj5SMA5SaOdbMxY\nWHIxcU1VXxdnOdf8QYeG+0s90l/wkeZSukxV6wft61LV/oDrlqnqqiHOfR9O8roG+KaInK6q/yki\nT+HM+7ZBRN6FM517YFV38qDrtLv/enDWaFk6QrzGnBJrczFxza3a8gInBx16BbjeLbMAmIUzuWAw\n+5cAZ44xlH1AvnsnhYj4ROR0EfEARar6Is7CaNlAuojMVWcm2ztwOgksAo4Ai91ZdrNwZjV+B3XW\n5zkkIte6ryUictYY4zVmRHbnYuLRQJsLOHcMN6hq/6BqpV8C/yMiO3HuCP7JbfMYbv+vcFabHJhN\nekxTp6tqj4h8GPiZmxgScNan2Q/8wd0nOGvZN4nId9zOBP040+L/xY3jYZxqtAPA1hFe8nrgVyLy\nDcCHsybO4BUIjRk3mxXZGGNMyFm1mDHGmJCz5GKMMSbkLLkYY4wJOUsuxhhjQs6SizHGmJCz5GKM\nMSbkLLkYY4wJOUsuxhhjQu7/AYayyldV/L54AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20815ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#舒张压\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.BloodPressure, kde = True)\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.ylabel('Number of BloodPressure')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个特征为0的已经很多的，应该为缺失值，不过基本也是正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of SkinThickness')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ltCA9pAUEB1Oa77RkRtu6+Ed5HbWtHZw+KzfssceChTMnIYK1LsyE5qVl8Yl+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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2077a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#三头肌皮肤褶层厚度\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.SkinThickness, kde = True)\n",
    "plt.xlabel('SkinThickness')\n",
    "plt.ylabel('Number of SkinThickness')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正常人皮肤褶层厚度不可能为0，这个的0可能是缺失值了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of Insulin')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bj0k6JSLWAYtyyt8K/CinvDsi4vbO6jbQ/f3PXua7KzYzZ8JwLl04hZNGl/d3lczMuqzd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\nW4FVEVFJEiweSDv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ujVMREcuBvyaZKBJgE3B2ur8EKO772pn1nP+VY9Z/RgI/kVRGMrPw\np9P0b6bpT5PMPnyon+pn1iOeTdnMzDLhR2RmZpYJBxgzM8uEA4yZmWXCAcbMzDLhAGNmZplwgDEz\ns0w4wJiZWSYcYMzMLBP/H637zJLk16rMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a207c5f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2小时血清胰岛素含量\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Insulin, kde = True)\n",
    "plt.xlabel('Insulin')\n",
    "plt.ylabel('Number of Insulin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，这个特征为0的特别多，这个0可能是缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of BIM')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CxK3Gzj6qm7utCyoKrlwyA59XuO+5w25HMVOEFQsTt3YcC45XrCmxmVCTLcPv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\nGGNMWP8fhWmzNi4gs/YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20aab1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#体重指数\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.BMI, kde = True)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('Number of BIM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体重不可能为为0，这里0是缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of DiabetesPedigreeFunction')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZowviHUZCe3hJ1YDbJ5XmkhXwcd2Dy1j47dPw+Qb6p2OMiQUvdwq3AnuBVcCX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\nY/pYUjDGGNPHkoIxxpg+/x+ucJOyT4tYtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fbe1128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#糖尿病家族史\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.DiabetesPedigreeFunction, kde = True)\n",
    "plt.xlabel('DiabetesPedigreeFunction')\n",
    "plt.ylabel('Number of DiabetesPedigreeFunction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of Age')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QzESHJ9JvqWK3Dy2vChpl1V4RvQlFQ/inSybyvz4wmec37OED33uFl96pTXRY\nIv2WkkUfWr6tgaz0FE4fMzTRoQwKaSkpfPbiiSy89TyG52Zw0wNv8c0/rqe1rV/0hRBJKkoWfWhZ\nVT1nFheQnqofe186bfRQnrz1PG6cV8aPX6rk4z97S0u+ihwnfWr1kYNHW1izvZE541QFlQhZ6al8\n40NncNc1Z7C4so6r7n2Vij0HEx2WSL+hZNFHlm6to7XNOWf8iESHMqhdN6eMRz99NkeaW7nmh6/z\n1pa6RIck0i8oWfSRNyvrSE81Zo1VT6hEm1k2jN/943kMH5LBR3+6mN+v6jxWVEQ6U7LoI29U7mN6\nSQE5GVqcMBmUDc/hd/94LtNL8/mnh1fwo5c2466Gb5HuKFn0gQNNx1izvZGzxw9PdCjSQUFOBg/e\nPI8rzhzDt/64gdufXKueUiLd0L+5fWDptvqgvWKCkkWyyUpP5fsLZlJckM2PX65k9/4mvn/9TK2n\nIdKJ7iz6wJuV+4L2ijL1hEpGKSnGFz94Ol+5cgrPrt/NjT9drK61Ip0oWfSBNzfvY0ZpAdkZ+m81\nmX3ivHH84IZZvL29kWt+9DrVdYcTHZJI0lCyiNiBpmO8rfaKfuPyM8bw0M3z2HvgKB/+4eus2d6Y\n6JBEkoLaLCL2ZmUdbQ7nKFkkxInOcPvbfzyXj/9sCdf+6HW+d90MLps2ppcjE+lfdGcRsRc37iE3\nI3XQT0ve30walcfjnz2X08cM5TMPLeeeFzapa60MakoWEXJ3Xtywh/MnjdDKbf3QyLwsHv7U2cyf\ncQrffuYdbnlwGY1HjiU6LJGE0CdYhDbsOsDOxiYuOW1kokORE5SVnsr3rpvBV66cwosb9nDVPa+y\nuqYh0WGJ9Dkliwi9sCFY2O/iyUoW/ZmZ8YnzxvHop8+muaWND//gde55YZMG8MmgEmmyMLPLzGyj\nmVWY2W1dPJ9pZo+Gzy82s/Lw+HAze9HMDprZPVHGGKUXN+xhWvFQRmq97QHhrLGF/OlzF3DZtNF8\n+5l3+PAPX2fdjv2JDkukT0TWG8rMUoF7gUuBGmCJmS1093Udit0M1Lv7RDNbANwFXAc0AV8GpoWP\nfqf+UDPLq+r57MUTEx2KnICeelGdM344uRlpPLV6B1f831c4b8IILj5tJJ88f1wfRijSt6LsOjsX\nqHD3SgAzewSYD3RMFvOBO8Ltx4B7zMzc/RDwqpn120/alzfV0uZwsdorBhwzY3ppAZNGDeFPa3bx\nSsVellXVk2Jww7yx6swgA1KU7+pioLrDfk14rMsy7t4CNAJxD0gws1vMbKmZLa2tTa71lZ9dt5vC\n3Ayml2hK8oEqJyOND88q4bOCt4BnAAAPx0lEQVQXTWT00Czu+P06Lvo/L/Lfb2yl6VhrosMT6VVR\nJgvr4ljnFsF4ynTL3e9z99nuPruoqOi4govSoaMtPLd+N5dPG01qSlffogwkxcOyufn8cfz8E3MY\nU5DN7U+u5dxvvcA3/7ieqn2aMkQGhiiroWqA0g77JUDnVWbay9SYWRqQD/T7pcueWbeLpmNtXD2z\n842UDFRmxkWTR3LhqUW8WVnHz17bwk9eruS+lyu5YFIRN84r45LTRpKm9deln4oyWSwBJpnZOGA7\nsAC4oVOZhcBNwBvAtcALPgCGyT6xYgfFBdmcpVlmB5WOjeIXTR7JzLJhLNlax9Ktdbz0Ti1DMtM4\noySf6SUFlA7Lxsy4YV5ZAiMWiV9kycLdW8zsVuBpIBV4wN3XmtmdwFJ3XwjcDzxoZhUEdxQL2s83\ns63AUCDDzK4G3t+pJ1VS2nvwKK9W7OWWC8aToiqoQS0/O533nT6KiyePZMOu/aysbmDJljre2LyP\nYTnpTC8p4MySfKaeMhQzvVckuUU6kaC7LwIWdTp2e4ftJuAj3ZxbHmVsUfnD6p20tjlXz1AVlARS\nU4ypp+Qz9ZR8mo61snbHflbXNPDSO7X8+Z1aiguyuXTKKN4/ZRRzxhWSrqoqSUKadbaXPbFyO6eN\nzmPy6LxEhyJJKCs9lbPGDuOsscM4eLSFgux0nlm3m4ffquLnr29laFYa7z19FJdOGcUFpxYxJFN/\nopIc9E7sRRt27WdFVQO3XX5aokORfmBIZhp/P6eUv59TyuHmFl5+Zy/PrtvN8xt28/iK7aSlGDNK\nCzh/0gjOnziC6aUFuuuQhFGy6EX3v7KF7PRUFswpjV1YpIOcjDQumzaay6aNpqW1jaXb6nn5nVpe\nq9jLfz2/ie89t4khmWmcPb6Q8yaO4NwJI5g0cojaxaTPKFn0kj0Hmnhy5Q6um1NKQU5GosORfqKn\naUVKhuVw3ZwyrpzeQmXtISr2HGR5VQPPrQ8mqMzLSmNW2bB3q7Wmlxao2koio3dWL3nwjW0ca2vT\n/EDS63Iy0phWnM+04nwA6g41Mzo/i2Xb6lm+rZ67n3sHd0gxOG300HeTx1ljh1ESdtEVOVlKFr3g\nSHMrD725jfedPopxI3ITHY4McIW5GVx7VgnXnlUCQOORY6ysbmDZtnpWVNXz+IrtPPjmNgCK8jKZ\nWVrAzLJhzCwLuurmZOjPXo6f3jW94OG3qqg/fIxPvWd8okORQaKr6qvRQ7O4fNoYPjB1NLv3N7Ft\n32Gq6w6zvKqeZ9btBoK7j9FDsygtzKG0MIeywhyG52b0ePehgYMCShYnre5QM9977h3OnziCOeUa\nsS2Jl2LGmPxsxuRnc/b4YF7Ow0dbqK4/TFXdEarrD7OyuoHFW4KZdbLTUykrzKG0MJuywlxKhmWT\nlZ6ayG9BkpCSxUn69jMbOdTcyleunKK6YUlaOZlpTB49lMmjhwLQ5k7tgaNU1x2mKny8s/sATjC7\nZ1FeJmXhncfs8mFMLFLPq8FOyeIkrNneyMNvVfGJc8cxaZQG4Un/kWLGqKFZjBqaxezyQgCajrVS\nU3+EqrpDVNcdYe2O/SzdVs/vVmwnLzONGWUF77Z/zCgtYFiuev0NJkoWJ6i5pY1/f2INhTkZfO59\nkxIdjshJy0pPZeLIIUwcOQQAd2ffwWaKh2Wzorqe5dsauOfFCtqXHh8/IjdIIGXDmFVWwORReZpV\ndwBTsjhBdz61llXVDdx7wyzys9MTHY5IrzMzRuRlcs1ZJVwT9rw6dLSFt7c3sqKqgeVVwcDB3y3f\nDgRtH2eW5L+bPGaXF1Kou48BQ8niBPx6STUPvVnFpy8cz9+dOSbR4YhEqqueV/nZ6Vw8eSQXnVpE\nw+FjQbtHfdD7aunWelrDlQamjBnKeROHc+7EEcwtLyRXgwb7Lf3mjtOLG/bwH0+s4T2TRvCvH9Ac\nUDK4mRnDcjMYlpvB9NJgCeFjrW3saDhC5d5g1PkDr23lJ69sIdWM0sJsJhQNYULREEoLc/5mJUl1\n001eShbH4dElVXzp8TWcPiaP7y+YqSVTRbqQnprC2OG5jB2ey8WTR9Lc0sa2ukNs3nOIzbUHeWHD\nHp7fsIeM1BTKR+QwsWgIE0YOYdTQrESHLj1QsojDwaMtfOeZjfzsta1ccGoRP7hxlubgEYlTRloK\nk0bmMWlk0GPwcHMw19Xm2oNsrj3Eot27AMjJSOWNyn2cN2EE500cTllhjrqjJ5FIP/HM7DLgvwhW\nyvupu3+r0/OZwH8DZwH7gOvcfWv43BeBm4FW4J/d/ekoY+3KsdY2/rB6J9/843r2HDjKx84Zy5ev\nmKJpokVOQue5rhqPHAsSx56DLNtazx9W7wSguCCbmWUFnFGczxnF+UwtzldnkgSKLFmYWSpwL3Ap\nUAMsMbOFnZZGvRmod/eJZrYAuAu4zsymECyxOhU4BXjOzE5199ao4m3X0trG29sbeXbdbn6zrIba\nA0eZespQfvTRs5ipNbVFel1+djqzyoYxq2wY188tZXPtIV7fvJc3Nu9jRVUDT4XJA6CsMIcJRbmU\nj8hl/Ihcxo0YQllhDiOHZmrUecSivLOYC1S4eyWAmT0CzAc6Jov5wB3h9mPAPRbcd84HHnH3o8CW\ncI3uucAbvR3knv1N/H71TiprD1JZe4jVNQ0cam4lxeCS00ayYE4ZF582Uu0TIn3AzN4d6/Gxc8qB\nYEqdNdsbeXt7I+t27mdL7SEWb6njcPNf/++Yl5lG0dBMioZkMiwng9zMNPKy0sjNTCU3M42M1BRS\nU4y0FCM1JYXUFEhNSSEtxWiv7TIz7N1YwOjwXHisfS94/i/npViwhG5qipFqRkq4nWJ/Ofbu8ym8\nezzlr47/9bnBNqSa0ebQ0tbGsVantc051trGoaMtHDraSm5mKuOLhkT3iyHaZFEMVHfYrwHmdVfG\n3VvMrBEYHh5/s9O5kSxqvefAUb721DqGZqUxvmgIV88s5pwJwzl7/HBGDMmM4pIi0o2e1vcYlpMR\ntGdMGIG7c6Cphb0Hj3Lq6DxqDxx997HnQBOVew9ysKmFg0dbONTcSmv7SMIB6oozx3DPDbMivUaU\nyaKrf8U7/8a6KxPPuZjZLcAt4e5BM9sYZ2wjgL2dD74NPBnnC0Sky7iSRLLGpriOj+I6Pv0irnuB\ne2884dcaG0+hKJNFDdBxfdESYEc3ZWrMLA3IB+riPBd3vw+473gDM7Ol7j77eM+LWrLGBckbm+I6\nPorr+Ciuv4iyW88SYJKZjTOzDIIG64WdyiwEbgq3rwVecHcPjy8ws0wzGwdMAt6KMFYREelBZHcW\nYRvErcDTBF1nH3D3tWZ2J7DU3RcC9wMPhg3YdQQJhbDcrwkaw1uAz/ZFTygREelapOMs3H0RsKjT\nsds7bDcBH+nm3G8A34gotOOuuuojyRoXJG9siuv4KK7jo7hC5j6wewmIiMjJ01BkERGJacAnCzN7\nwMz2mNmaDscKzexZM9sUfu3zodlmVmpmL5rZejNba2afS4bYzCzLzN4ys1VhXF8Nj48zs8VhXI+G\nnRb6nJmlmtkKM3sqWeIys61m9raZrTSzpeGxZHiPFZjZY2a2IXyfnZMkcU0Of1btj/1m9vkkie1/\nhu/7NWb2cPj3kAzvsc+FMa01s8+Hx/r05zXgkwXwc+CyTsduA55390nA8+F+X2sBvuDupwNnA58N\npzlJdGxHgUvcfTowA7jMzM4mmIrl7jCueoKpWhLhc8D6DvvJEtfF7j6jQ3fGRP8eIZiX7U/ufhow\nneDnlvC43H1j+LOaQTAv3GHg8UTHZmbFwD8Ds919GkHHnPZpiBL2HjOzacCnCGaxmA5cYWaT6Ouf\nl7sP+AdQDqzpsL8RGBNujwE2JkGMTxLMo5U0sQE5wHKCkfd7gbTw+DnA0wmIpyT8o7gEeIpg8GYy\nxLUVGNHpWEJ/j8BQYAthu2SyxNVFnO8HXkuG2PjLjBKFBJ1/ngI+kOj3GEEnoJ922P8y8K99/fMa\nDHcWXRnl7jsBwq8jExmMmZUDM4HFJEFsYVXPSmAP8CywGWhw95awSGTTr8TwPYI/krZwf3iSxOXA\nM2a2LJxVABL/exwP1AI/C6vtfmpmuUkQV2cLgIfD7YTG5u7bgW8DVcBOoBFYRuLfY2uAC8xsuJnl\nAB8kGLTcpz+vwZoskoaZDQF+C3ze3fcnOh4Ad2/1oIqghODW9/SuivVlTGZ2BbDH3Zd1PNxF0UR0\n7zvP3WcBlxNUJ16QgBg6SwNmAT9095nAIRJTFdatsO7/KuA3iY4FIKzznw+MI5jtOpfgd9pZn77H\n3H09QVXYs8CfgFUE1dh9arAmi91mNgYg/LonEUGYWTpBovilu/8umWIDcPcG4M8EbSoFFkzJAt1M\nvxKx84CrzGwr8AhBVdT3kiAu3H1H+HUPQd37XBL/e6wBatx9cbj/GEHySHRcHV0OLHf33eF+omN7\nH7DF3Wvd/RjwO+BckuM9dr+7z3L3CwgGMG+ij39egzVZdJxm5CYSMH+gmRnBCPb17v7dZInNzIrM\nrCDczib4A1oPvEgwJUtC4nL3L7p7ibuXE1RdvODuNyY6LjPLNbO89m2COvg1JPj36O67gGozmxwe\nei/BjAgJf+93cD1/qYKCxMdWBZxtZjnh32f7zyyh7zEAMxsZfi0DPkzwc+vbn1dfNtQk4hH+UHcC\nxwj+27qZoK77eYLs/DxQmIC4zie4nV0NrAwfH0x0bMCZwIowrjXA7eHx8QTzc1UQVBtkJvB3ehHw\nVDLEFV5/VfhYC/x7eDwZ3mMzgKXh7/IJYFgyxBXGlkOwOmZ+h2MJjw34KrAhfO8/CGQm+j0WxvUK\nQeJaBbw3ET8vjeAWEZGYBms1lIiIHAclCxERiUnJQkREYlKyEBGRmJQsREQkJiULkV5gZh8yMzez\n0xIdi0gUlCxEesf1wKuESwOLDDRKFiInKZzf6zyCAZ8LwmMpZvaDcP2Bp8xskZldGz53lpm9FE48\n+HT7lA0iyUzJQuTkXU2wbsQ7QJ2ZzSKYkqEcOAP4HwRTW7fPB/Z/gWvd/SzgAaJba16k16TFLiIi\nMVxPMKkhBJMcXg+kA79x9zZgl5m9GD4/GZgGPBtMP0QqwXQ0IklNyULkJJjZcIIZcKeZmRN8+DvB\n7LNdngKsdfdz+ihEkV6haiiRk3Mt8N/uPtbdy929lGCFur3ANWHbxSiCyQ8hWN2syMzerZYys6mJ\nCFzkeChZiJyc6/nbu4jfEiyeU0Mwe+mPCVZBbHT3ZoIEc5eZrSKYbfjcvgtX5MRo1lmRiJjZEHc/\nGFZVvUWwot6uRMclciLUZiESnafChaQygK8pUUh/pjsLERGJSW0WIiISk5KFiIjEpGQhIiIxKVmI\niEhMShYiIhKTkoWIiMT0/wCczoF4fpthEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20c11438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#年龄\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Age, kde = True)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "样本中年轻人较多=。="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20e0ef28>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zkeoeMRFHI9m7Yz/2mnMgrapYqRz79x/i0MEjxMTEMPnnmTRqXNcjpmHjukwY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\nA/M+hUxSV8gDOwN3AlvYuh9y16bBJE2NT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20ec3550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看特征之间的两两相关\n",
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现特征之间貌似只有怀孕和年龄之间的相关系较大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                   0\n",
      "Glucose                       5\n",
      "BloodPressure                35\n",
      "SkinThickness               227\n",
      "Insulin                     374\n",
      "BMI                          11\n",
      "DiabetesPedigreeFunction      0\n",
      "Age                           0\n",
      "Outcome                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#这里全部将0替换为空值，方便后面的缺失值处理，同样体现了在Pandas和numpy的强大，批量处理空值，妈妈再也不用担心我报空指针异常了\n",
    "NaN_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0, np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#这里的空值使用均值进行填补\n",
    "medians = train.median() \n",
    "train = train.fillna(medians)\n",
    "\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>121.656250</td>\n",
       "      <td>72.386719</td>\n",
       "      <td>29.108073</td>\n",
       "      <td>140.671875</td>\n",
       "      <td>32.455208</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>30.438286</td>\n",
       "      <td>12.096642</td>\n",
       "      <td>8.791221</td>\n",
       "      <td>86.383060</td>\n",
       "      <td>6.875177</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.750000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>121.500000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  121.656250      72.386719      29.108073  140.671875   \n",
       "std       3.369578   30.438286      12.096642       8.791221   86.383060   \n",
       "min       0.000000   44.000000      24.000000       7.000000   14.000000   \n",
       "25%       1.000000   99.750000      64.000000      25.000000  121.500000   \n",
       "50%       3.000000  117.000000      72.000000      29.000000  125.000000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    32.455208                  0.471876   33.240885    0.348958  \n",
       "std      6.875177                  0.331329   11.760232    0.476951  \n",
       "min     18.200000                  0.078000   21.000000    0.000000  \n",
       "25%     27.500000                  0.243750   24.000000    0.000000  \n",
       "50%     32.300000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看特征处理后的数据\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里可以将进行过特征处理的数据保存为文件，这样就可以不用每次要修改、优化模型及代码的时候，从头在跑代码了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4、特征编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 貌似这里的数据都是数值型的，不需要编码\n",
    "y = train['Outcome']\n",
    "X = train.drop([\"Outcome\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
      "       'BMI', 'DiabetesPedigreeFunction', 'Age'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#训练数据和测试数据分割\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=4)\n",
    "columns = X_train.columns\n",
    "\n",
    "print(columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "题目要求随机选择 20%的数据作为测试集，随机分割出测试数据与训练数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5、数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6、模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先使用缺省的Logistic回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [ 0.43241075  0.4444343   0.50935307  0.51989921  0.51251818]\n",
      "cv logloss is: 0.483723102055\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 交叉验证用于评估模型性能和进行参数调优（模型选择）\n",
    "#分类任务中交叉验证缺省是采用StratifiedKFold\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print ('logloss of each fold is: ',-loss)\n",
    "print ('cv logloss is:', -loss.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先使用缺省大致了解Logistic回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 正则化的Logistic回归及参数调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "logistic回归的需要调整超参数有：C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和正则函数penalty（L2/L1） \n",
    "目标函数为：J = sum(logloss(f(xi), yi)) + C* penalty \n",
    "\n",
    "在sklearn框架下，不同学习器的参数调整步骤相同：\n",
    "设置候选参数集合\n",
    "调用GridSearchCV\n",
    "调用fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                   'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5,scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuary of default Logistic Regression is 1.0\n"
     ]
    }
   ],
   "source": [
    "lr_penalty.fit(X_train,y_train)\n",
    "\n",
    "y_prob = lr_penalty.predict_proba(X_test)[:,1] # This will give you positive class prediction probabilities  \n",
    "y_pred = np.where(y_prob > 0.5, 1, 0) # This will threshold the probabilities to give class predictions.\n",
    "   \n",
    "#accuracy \n",
    "print ('The accuary of default Logistic Regression is',lr_penalty.score(X_test, y_pred) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The AUC of default Logistic Regression is 0.5\n"
     ]
    }
   ],
   "source": [
    "print ('The AUC of default Logistic Regression is', roc_auc_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.00118346,  0.00118198,  0.00085521,  0.00143576,  0.00205784,\n",
       "         0.00148644,  0.00099978,  0.00088396,  0.00090232,  0.00078173,\n",
       "         0.00084271,  0.00088792,  0.00083661,  0.00078578]),\n",
       " 'mean_score_time': array([ 0.00106816,  0.00106754,  0.00082684,  0.00128689,  0.00159178,\n",
       "         0.00119977,  0.00065179,  0.00070848,  0.00070672,  0.00067196,\n",
       "         0.00063262,  0.00060964,  0.00065303,  0.00060444]),\n",
       " 'mean_test_score': array([-0.69314718, -0.63743715, -0.6792335 , -0.52646635, -0.48845785,\n",
       "        -0.48418657, -0.48306291, -0.48353372, -0.48382969, -0.48390725,\n",
       "        -0.48394674, -0.48395254, -0.48395609, -0.48395715]),\n",
       " 'mean_train_score': array([-0.69314718, -0.63628171, -0.67769396, -0.52109684, -0.47836184,\n",
       "        -0.47192476, -0.46842746, -0.46830459, -0.46824419, -0.46824283,\n",
       "        -0.46824219, -0.46824217, -0.46824216, -0.46824216]),\n",
       " 'param_C': masked_array(data = [0.001 0.001 0.01 0.01 0.1 0.1 1 1 10 10 100 100 1000 1000],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False],\n",
       "        fill_value = ?),\n",
       " 'param_penalty': masked_array(data = ['l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2'],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'rank_test_score': array([14, 12, 13, 11, 10,  9,  1,  2,  3,  4,  5,  6,  7,  8], dtype=int32),\n",
       " 'split0_test_score': array([-0.69314718, -0.6321351 , -0.6778988 , -0.50423496, -0.45653375,\n",
       "        -0.44048777, -0.43273701, -0.43241075, -0.43173522, -0.43179792,\n",
       "        -0.4317341 , -0.43174082, -0.43173508, -0.43173516]),\n",
       " 'split0_train_score': array([-0.69314718, -0.63949375, -0.67765416, -0.53059503, -0.49155075,\n",
       "        -0.48547383, -0.48230672, -0.48219918, -0.48214495, -0.48214341,\n",
       "        -0.48214283, -0.48214281, -0.48214281, -0.4821428 ]),\n",
       " 'split1_test_score': array([-0.69314718, -0.63314044, -0.69187808, -0.50650482, -0.45548232,\n",
       "        -0.45010455, -0.4445199 , -0.4444343 , -0.44405468, -0.44405012,\n",
       "        -0.44402001, -0.4440159 , -0.44401187, -0.44401252]),\n",
       " 'split1_train_score': array([-0.69314718, -0.63899484, -0.69224717, -0.52909834, -0.48816301,\n",
       "        -0.48218192, -0.47898399, -0.47888173, -0.47882822, -0.47882714,\n",
       "        -0.47882658, -0.47882656, -0.47882656, -0.47882656]),\n",
       " 'split2_test_score': array([-0.69314718, -0.64268292, -0.67504146, -0.54287469, -0.50718443,\n",
       "        -0.50709531, -0.50782636, -0.50935307, -0.51001753, -0.51018945,\n",
       "        -0.51026672, -0.51028355, -0.51029064, -0.51029307]),\n",
       " 'split2_train_score': array([-0.69314718, -0.63353056, -0.67115489, -0.51552367, -0.47180453,\n",
       "        -0.46483765, -0.4610975 , -0.46095046, -0.46088434, -0.4608828 ,\n",
       "        -0.4608821 , -0.46088208, -0.46088207, -0.46088207]),\n",
       " 'split3_test_score': array([-0.69314718, -0.64071738, -0.67520028, -0.54169809, -0.52057475,\n",
       "        -0.51488962, -0.52071559, -0.51989921, -0.52113004, -0.52105994,\n",
       "        -0.5211926 , -0.52118733, -0.52120139, -0.52120019]),\n",
       " 'split3_train_score': array([-0.69314718, -0.63422011, -0.67271779, -0.51446306, -0.4671725 ,\n",
       "        -0.4621326 , -0.45830361, -0.45816356, -0.45809644, -0.45809503,\n",
       "        -0.45809431, -0.4580943 , -0.45809429, -0.45809429]),\n",
       " 'split4_test_score': array([-0.69314718, -0.63858905, -0.67610147, -0.53741276, -0.50315416,\n",
       "        -0.50916355, -0.51045361, -0.51251818, -0.51317639, -0.51340433,\n",
       "        -0.51348771, -0.51350254, -0.51350909, -0.51351246]),\n",
       " 'split4_train_score': array([-0.69314718, -0.6351693 , -0.67469579, -0.51580412, -0.47311841,\n",
       "        -0.4649978 , -0.46144551, -0.46132802, -0.461267  , -0.46126577,\n",
       "        -0.46126511, -0.4612651 , -0.4612651 , -0.4612651 ]),\n",
       " 'std_fit_time': array([  5.29113719e-04,   3.51655446e-04,   2.08163315e-04,\n",
       "          3.34926258e-04,   6.14766001e-04,   4.64310413e-04,\n",
       "          1.51801811e-04,   7.34927503e-05,   1.27893966e-04,\n",
       "          4.85389780e-05,   1.30637248e-04,   1.12507037e-04,\n",
       "          6.90693315e-05,   4.58437206e-05]),\n",
       " 'std_score_time': array([  2.07072563e-04,   2.76595007e-04,   1.68481016e-04,\n",
       "          3.10606930e-04,   4.30461601e-04,   2.46509334e-04,\n",
       "          6.30968182e-05,   1.10725716e-04,   1.09533491e-04,\n",
       "          6.67566231e-05,   8.02444261e-05,   3.90153629e-05,\n",
       "          1.12839360e-04,   5.78416249e-05]),\n",
       " 'std_test_score': array([ 0.        ,  0.0041581 ,  0.00641019,  0.0174206 ,  0.02723553,\n",
       "         0.0321674 ,  0.03691479,  0.03738057,  0.03807827,  0.03810699,\n",
       "         0.03818013,  0.03818271,  0.03818985,  0.03819032]),\n",
       " 'std_train_score': array([ 0.        ,  0.0024793 ,  0.00759353,  0.00717384,  0.00965094,\n",
       "         0.00982734,  0.01009002,  0.01010477,  0.01011018,  0.0101102 ,\n",
       "         0.01011026,  0.01011026,  0.01011026,  0.01011026])}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the complete results (list of named tuples)\n",
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.765472312704\n",
      "{'C': 1, 'gamma': 0.1}\n"
     ]
    }
   ],
   "source": [
    "# 打印最好的模型\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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9wBHKNdFL2fzDIVbvKvB3RMYYH/I2WfwJ+E5E/iUirwGLgD/6LqzgFtqsGfE3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0fe12780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = -grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = -grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores =  np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'logloss' )\n",
    "pyplot.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 换正确率作为评价标准"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.760586319218\n",
      "{'C': 0.1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "\n",
    "#缺省scoring为正确率\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5)\n",
    "grid.fit(X_train,y_train)\n",
    "\n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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h/MmEd05L5/e751fx0c4qZn99Imnq1JZuYMwNrya7hDbTXvuSaAf3U2ZWSBAQ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\nWIiISFwKCxERiev/AQEFozPv5oNBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0fe12438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores =  np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "#train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "#train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    #pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuary' )\n",
    "pyplot.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用线性SVM训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.771986970684\n",
      "{'C': 0.01}\n"
     ]
    }
   ],
   "source": [
    "#线性SVM\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "param_grid = {'C': Cs}\n",
    "grid = GridSearchCV(SVC(kernel='linear'), param_grid, cv=5)\n",
    "\n",
    "grid.fit(X_train, y_train)\n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RBF核SVM正则参数调优\n",
    "\n",
    "RBF核是SVM最常用的核函数。\n",
    "RBF核SVM 的需要调整正则超参数包括C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和核函数的宽度gamma\n",
    "C越小，决策边界越平滑； \n",
    "gamma越小，决策边界越平滑。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000], 'gamma': [0.0001, 0.001, 0.01, 0.1, 1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "gammas = [0.0001,0.001, 0.01, 0.1, 1]\n",
    "param_grid = {'C': Cs, 'gamma' : gammas}\n",
    "grid = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=5)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.765472312704\n",
      "{'C': 1, 'gamma': 0.1}\n"
     ]
    },
    {
     "data": {
      "image/png": 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GPSU0NtmYqaWgusxZk4ZSkksuuYRLLrnkhGOPP/74sb/d3Nz4/PPP2zz30KFD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\nZ66oJ38AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0fde7080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "\n",
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_gamms = len(gammas)\n",
    "\n",
    "test_scores =  np.array(test_means).reshape(n_Cs,number_gamms)\n",
    "#train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_gamms)\n",
    "#train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(gammas):\n",
    "    pyplot.plot(x_axis, test_scores[:,i], label= gammas[i])\n",
    "    #pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = gammas[i] )\n",
    "    #pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuary' )\n",
    "pyplot.savefig('SVMGridSearchCV_C.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/anaconda2/envs/python36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.00606642,  0.00453329,  0.00441532,  0.0044466 ,  0.00440035,\n",
       "         0.00745077,  0.00549641,  0.00423484,  0.00441194,  0.00545783,\n",
       "         0.00425625,  0.00424142,  0.00423179,  0.00426259,  0.00572238,\n",
       "         0.00421438,  0.00424833,  0.00393343,  0.00443897,  0.00712872,\n",
       "         0.00473256,  0.00447707,  0.00479612,  0.00637765,  0.00860667,\n",
       "         0.00436602,  0.00468688,  0.007548  ,  0.01684041,  0.00856123,\n",
       "         0.00471706,  0.00836558,  0.03848   ,  0.04028144,  0.00811605]),\n",
       " 'mean_score_time': array([ 0.00159831,  0.00108299,  0.00108061,  0.00112138,  0.00108061,\n",
       "         0.00267234,  0.00116696,  0.00099645,  0.00101919,  0.00123415,\n",
       "         0.00099535,  0.00100045,  0.00104346,  0.00097432,  0.00118241,\n",
       "         0.00096998,  0.00098376,  0.00087948,  0.0009285 ,  0.00140424,\n",
       "         0.0011168 ,  0.00113802,  0.00104661,  0.00093575,  0.0014061 ,\n",
       "         0.00108261,  0.00094333,  0.00095701,  0.0009582 ,  0.00132322,\n",
       "         0.00094442,  0.00094943,  0.00102091,  0.00087509,  0.00121002]),\n",
       " 'mean_test_score': array([ 0.64820847,  0.64820847,  0.64820847,  0.64820847,  0.64820847,\n",
       "         0.64820847,  0.64820847,  0.64820847,  0.64820847,  0.64820847,\n",
       "         0.64820847,  0.64820847,  0.64820847,  0.74104235,  0.64820847,\n",
       "         0.64820847,  0.65960912,  0.75732899,  0.76547231,  0.67589577,\n",
       "         0.66123779,  0.75570033,  0.752443  ,  0.73615635,  0.67752443,\n",
       "         0.75407166,  0.747557  ,  0.76221498,  0.68729642,  0.67589577,\n",
       "         0.75895765,  0.752443  ,  0.74918567,  0.66775244,  0.67589577]),\n",
       " 'mean_train_score': array([ 0.64820865,  0.64820865,  0.64820865,  0.64820865,  0.64820865,\n",
       "         0.64820865,  0.64820865,  0.64820865,  0.64820865,  0.64820865,\n",
       "         0.64820865,  0.64820865,  0.64861515,  0.76219636,  0.64820865,\n",
       "         0.64820865,  0.65838036,  0.77074869,  0.82654998,  0.97313095,\n",
       "         0.65878852,  0.76544921,  0.78622485,  0.88273622,  1.        ,\n",
       "         0.7658582 ,  0.77563585,  0.81636417,  0.95399544,  1.        ,\n",
       "         0.76952585,  0.78785501,  0.84853601,  0.99470716,  1.        ]),\n",
       " 'param_C': masked_array(data = [0.001 0.001 0.001 0.001 0.001 0.01 0.01 0.01 0.01 0.01 0.1 0.1 0.1 0.1 0.1\n",
       "  1 1 1 1 1 10 10 10 10 10 100 100 100 100 100 1000 1000 1000 1000 1000],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False False False False False False False False False False False\n",
       "  False False False False False False False False False False False],\n",
       "        fill_value = ?),\n",
       " 'param_gamma': masked_array(data = [0.0001 0.001 0.01 0.1 1 0.0001 0.001 0.01 0.1 1 0.0001 0.001 0.01 0.1 1\n",
       "  0.0001 0.001 0.01 0.1 1 0.0001 0.001 0.01 0.1 1 0.0001 0.001 0.01 0.1 1\n",
       "  0.0001 0.001 0.01 0.1 1],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False False False False False False False False False False False\n",
       "  False False False False False False False False False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'C': 0.001, 'gamma': 0.0001},\n",
       "  {'C': 0.001, 'gamma': 0.001},\n",
       "  {'C': 0.001, 'gamma': 0.01},\n",
       "  {'C': 0.001, 'gamma': 0.1},\n",
       "  {'C': 0.001, 'gamma': 1},\n",
       "  {'C': 0.01, 'gamma': 0.0001},\n",
       "  {'C': 0.01, 'gamma': 0.001},\n",
       "  {'C': 0.01, 'gamma': 0.01},\n",
       "  {'C': 0.01, 'gamma': 0.1},\n",
       "  {'C': 0.01, 'gamma': 1},\n",
       "  {'C': 0.1, 'gamma': 0.0001},\n",
       "  {'C': 0.1, 'gamma': 0.001},\n",
       "  {'C': 0.1, 'gamma': 0.01},\n",
       "  {'C': 0.1, 'gamma': 0.1},\n",
       "  {'C': 0.1, 'gamma': 1},\n",
       "  {'C': 1, 'gamma': 0.0001},\n",
       "  {'C': 1, 'gamma': 0.001},\n",
       "  {'C': 1, 'gamma': 0.01},\n",
       "  {'C': 1, 'gamma': 0.1},\n",
       "  {'C': 1, 'gamma': 1},\n",
       "  {'C': 10, 'gamma': 0.0001},\n",
       "  {'C': 10, 'gamma': 0.001},\n",
       "  {'C': 10, 'gamma': 0.01},\n",
       "  {'C': 10, 'gamma': 0.1},\n",
       "  {'C': 10, 'gamma': 1},\n",
       "  {'C': 100, 'gamma': 0.0001},\n",
       "  {'C': 100, 'gamma': 0.001},\n",
       "  {'C': 100, 'gamma': 0.01},\n",
       "  {'C': 100, 'gamma': 0.1},\n",
       "  {'C': 100, 'gamma': 1},\n",
       "  {'C': 1000, 'gamma': 0.0001},\n",
       "  {'C': 1000, 'gamma': 0.001},\n",
       "  {'C': 1000, 'gamma': 0.01},\n",
       "  {'C': 1000, 'gamma': 0.1},\n",
       "  {'C': 1000, 'gamma': 1}],\n",
       " 'rank_test_score': array([21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 11, 21, 21, 20,\n",
       "         4,  1, 15, 19,  5,  7, 12, 14,  6, 10,  2, 13, 15,  3,  7,  9, 18,\n",
       "        15], dtype=int32),\n",
       " 'split0_test_score': array([ 0.64516129,  0.64516129,  0.64516129,  0.64516129,  0.64516129,\n",
       "         0.64516129,  0.64516129,  0.64516129,  0.64516129,  0.64516129,\n",
       "         0.64516129,  0.64516129,  0.64516129,  0.78225806,  0.64516129,\n",
       "         0.64516129,  0.65322581,  0.81451613,  0.80645161,  0.62903226,\n",
       "         0.65322581,  0.81451613,  0.7983871 ,  0.72580645,  0.61290323,\n",
       "         0.81451613,  0.7983871 ,  0.78225806,  0.59677419,  0.61290323,\n",
       "         0.81451613,  0.7983871 ,  0.74193548,  0.62096774,  0.61290323]),\n",
       " 'split0_train_score': array([ 0.64897959,  0.64897959,  0.64897959,  0.64897959,  0.64897959,\n",
       "         0.64897959,  0.64897959,  0.64897959,  0.64897959,  0.64897959,\n",
       "         0.64897959,  0.64897959,  0.64897959,  0.74693878,  0.64897959,\n",
       "         0.64897959,  0.65306122,  0.75714286,  0.83061224,  0.9755102 ,\n",
       "         0.65306122,  0.74081633,  0.77346939,  0.88367347,  1.        ,\n",
       "         0.74489796,  0.75714286,  0.81428571,  0.96326531,  1.        ,\n",
       "         0.74897959,  0.77346939,  0.85306122,  0.99591837,  1.        ]),\n",
       " 'split1_test_score': array([ 0.6504065 ,  0.6504065 ,  0.6504065 ,  0.6504065 ,  0.6504065 ,\n",
       "         0.6504065 ,  0.6504065 ,  0.6504065 ,  0.6504065 ,  0.6504065 ,\n",
       "         0.6504065 ,  0.6504065 ,  0.6504065 ,  0.77235772,  0.6504065 ,\n",
       "         0.6504065 ,  0.6504065 ,  0.76422764,  0.76422764,  0.71544715,\n",
       "         0.65853659,  0.76422764,  0.77235772,  0.69918699,  0.73170732,\n",
       "         0.76422764,  0.76422764,  0.76422764,  0.71544715,  0.73170732,\n",
       "         0.7804878 ,  0.77235772,  0.71544715,  0.66666667,  0.73170732]),\n",
       " 'split1_train_score': array([ 0.64765784,  0.64765784,  0.64765784,  0.64765784,  0.64765784,\n",
       "         0.64765784,  0.64765784,  0.64765784,  0.64765784,  0.64765784,\n",
       "         0.64765784,  0.64765784,  0.64765784,  0.74745418,  0.64765784,\n",
       "         0.64765784,  0.65580448,  0.76985743,  0.82892057,  0.97759674,\n",
       "         0.65580448,  0.76782077,  0.78207739,  0.88187373,  1.        ,\n",
       "         0.76171079,  0.77596741,  0.81059063,  0.94704684,  1.        ,\n",
       "         0.76985743,  0.79226069,  0.84928717,  0.99389002,  1.        ]),\n",
       " 'split2_test_score': array([ 0.6504065 ,  0.6504065 ,  0.6504065 ,  0.6504065 ,  0.6504065 ,\n",
       "         0.6504065 ,  0.6504065 ,  0.6504065 ,  0.6504065 ,  0.6504065 ,\n",
       "         0.6504065 ,  0.6504065 ,  0.6504065 ,  0.73170732,  0.6504065 ,\n",
       "         0.6504065 ,  0.66666667,  0.7804878 ,  0.78861789,  0.6504065 ,\n",
       "         0.66666667,  0.77235772,  0.76422764,  0.77235772,  0.67479675,\n",
       "         0.76422764,  0.76422764,  0.79674797,  0.69105691,  0.67479675,\n",
       "         0.76422764,  0.75609756,  0.79674797,  0.68292683,  0.67479675]),\n",
       " 'split2_train_score': array([ 0.64765784,  0.64765784,  0.64765784,  0.64765784,  0.64765784,\n",
       "         0.64765784,  0.64765784,  0.64765784,  0.64765784,  0.64765784,\n",
       "         0.64765784,  0.64765784,  0.64765784,  0.76171079,  0.64765784,\n",
       "         0.64765784,  0.65376782,  0.75763747,  0.82281059,  0.97352342,\n",
       "         0.65784114,  0.75560081,  0.78411405,  0.88187373,  1.        ,\n",
       "         0.75967413,  0.77393075,  0.81670061,  0.95519348,  1.        ,\n",
       "         0.76578411,  0.78411405,  0.84317719,  0.99389002,  1.        ]),\n",
       " 'split3_test_score': array([ 0.64754098,  0.64754098,  0.64754098,  0.64754098,  0.64754098,\n",
       "         0.64754098,  0.64754098,  0.64754098,  0.64754098,  0.64754098,\n",
       "         0.64754098,  0.64754098,  0.64754098,  0.72131148,  0.64754098,\n",
       "         0.64754098,  0.66393443,  0.71311475,  0.72131148,  0.69672131,\n",
       "         0.66393443,  0.71311475,  0.71311475,  0.73770492,  0.68852459,\n",
       "         0.71311475,  0.68852459,  0.73770492,  0.72131148,  0.68852459,\n",
       "         0.70491803,  0.71311475,  0.7295082 ,  0.71311475,  0.68852459]),\n",
       " 'split3_train_score': array([ 0.64837398,  0.64837398,  0.64837398,  0.64837398,  0.64837398,\n",
       "         0.64837398,  0.64837398,  0.64837398,  0.64837398,  0.64837398,\n",
       "         0.64837398,  0.64837398,  0.6504065 ,  0.7703252 ,  0.64837398,\n",
       "         0.64837398,  0.66666667,  0.79065041,  0.82520325,  0.97154472,\n",
       "         0.66666667,  0.78252033,  0.79878049,  0.87804878,  1.        ,\n",
       "         0.78252033,  0.78861789,  0.82723577,  0.95528455,  1.        ,\n",
       "         0.78455285,  0.79674797,  0.8495935 ,  0.99796748,  1.        ]),\n",
       " 'split4_test_score': array([ 0.64754098,  0.64754098,  0.64754098,  0.64754098,  0.64754098,\n",
       "         0.64754098,  0.64754098,  0.64754098,  0.64754098,  0.64754098,\n",
       "         0.64754098,  0.64754098,  0.64754098,  0.69672131,  0.64754098,\n",
       "         0.64754098,  0.66393443,  0.71311475,  0.74590164,  0.68852459,\n",
       "         0.66393443,  0.71311475,  0.71311475,  0.74590164,  0.68032787,\n",
       "         0.71311475,  0.72131148,  0.7295082 ,  0.71311475,  0.67213115,\n",
       "         0.7295082 ,  0.72131148,  0.76229508,  0.6557377 ,  0.67213115]),\n",
       " 'split4_train_score': array([ 0.64837398,  0.64837398,  0.64837398,  0.64837398,  0.64837398,\n",
       "         0.64837398,  0.64837398,  0.64837398,  0.64837398,  0.64837398,\n",
       "         0.64837398,  0.64837398,  0.64837398,  0.78455285,  0.64837398,\n",
       "         0.64837398,  0.66260163,  0.77845528,  0.82520325,  0.96747967,\n",
       "         0.66056911,  0.7804878 ,  0.79268293,  0.88821138,  1.        ,\n",
       "         0.7804878 ,  0.78252033,  0.81300813,  0.94918699,  1.        ,\n",
       "         0.77845528,  0.79268293,  0.84756098,  0.99186992,  1.        ]),\n",
       " 'std_fit_time': array([  1.36521360e-03,   3.65807204e-04,   1.40296432e-04,\n",
       "          7.58359394e-05,   1.71212072e-04,   2.64963966e-03,\n",
       "          1.21785950e-03,   7.17322859e-05,   1.05143296e-04,\n",
       "          1.10728981e-04,   8.97342414e-05,   8.34729834e-05,\n",
       "          4.41033387e-05,   4.01511611e-05,   7.55034563e-05,\n",
       "          2.55980292e-05,   6.86993776e-05,   1.33142976e-04,\n",
       "          1.99078948e-04,   2.53644561e-04,   5.56352284e-04,\n",
       "          3.68743955e-04,   2.84639897e-04,   4.56617266e-04,\n",
       "          2.66376149e-04,   2.61933452e-04,   1.60868943e-04,\n",
       "          5.21004237e-04,   5.17385663e-04,   6.23108861e-05,\n",
       "          1.62066660e-04,   4.59783400e-04,   4.90218861e-03,\n",
       "          1.99952564e-03,   1.94455304e-04]),\n",
       " 'std_score_time': array([  4.88613605e-04,   7.19979266e-05,   3.64022621e-05,\n",
       "          1.00390768e-04,   4.10289489e-05,   2.41872240e-03,\n",
       "          1.98024943e-04,   2.06165772e-05,   3.09093482e-05,\n",
       "          2.34158605e-05,   2.22246019e-05,   3.73675471e-05,\n",
       "          1.17925946e-04,   1.04845638e-05,   1.24860336e-05,\n",
       "          4.61966375e-06,   1.86481740e-05,   2.09284258e-05,\n",
       "          3.58823051e-05,   1.28826046e-04,   7.62479465e-05,\n",
       "          1.48061692e-04,   1.72258465e-04,   5.36255062e-05,\n",
       "          1.49555138e-04,   1.48394328e-04,   7.12022786e-05,\n",
       "          7.64976702e-05,   6.78070151e-05,   8.67864588e-05,\n",
       "          6.89647985e-05,   6.47265651e-05,   1.06823124e-04,\n",
       "          7.14816276e-05,   1.78654282e-05]),\n",
       " 'std_test_score': array([ 0.00199699,  0.00199699,  0.00199699,  0.00199699,  0.00199699,\n",
       "         0.00199699,  0.00199699,  0.00199699,  0.00199699,  0.00199699,\n",
       "         0.00199699,  0.00199699,  0.00199699,  0.03200999,  0.00199699,\n",
       "         0.00199699,  0.00652739,  0.03942468,  0.03017232,  0.03170046,\n",
       "         0.00481866,  0.0385935 ,  0.0338854 ,  0.02402721,  0.03819097,\n",
       "         0.03802352,  0.03824169,  0.02554906,  0.04667761,  0.03821145,\n",
       "         0.03839767,  0.03175405,  0.02833547,  0.03042947,  0.03821145]),\n",
       " 'std_train_score': array([ 0.00050116,  0.00050116,  0.00050116,  0.00050116,  0.00050116,\n",
       "         0.00050116,  0.00050116,  0.00050116,  0.00050116,  0.00050116,\n",
       "         0.00050116,  0.00050116,  0.00102302,  0.01425659,  0.00050116,\n",
       "         0.00050116,  0.00534202,  0.01275372,  0.00281894,  0.00347019,\n",
       "         0.00464413,  0.0156651 ,  0.00876025,  0.00329593,  0.        ,\n",
       "         0.01404583,  0.01059186,  0.00578311,  0.00566448,  0.        ,\n",
       "         0.01218035,  0.00827943,  0.00321956,  0.00207278,  0.        ])}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.765472312704\n",
      "{'C': 1, 'gamma': 0.1}\n"
     ]
    }
   ],
   "source": [
    "# 打印最好的模型\n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
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